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共轭梯度法

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Finally, the numerical results show this class of conjugate gradient methods is very efficient.

然后证明三种新算法在强Wolfe线搜索下的全局收敛性,数值验证表明这类共轭梯度法是比较有效的。

Nonlinear conjugate gradient methods are a class of important methods for optimization.

中文摘要:非线性共轭梯度法是最优化中一种重要的方法。

In this paper a class of new conjugate gradient methods are presented, with which the global convergence with generalized Wolfe line search is proven.

本文提出一族新的共轭梯度法,证明了其在推广的Wolfe非精确线搜索条件下具有全局收敛性。

The two Armijo-type line searches proposed in Chapter 4 are shown to guarantee the global convergence of the class of conjugate gradient methods.

在第四章提出的两种Armijo型线搜索下,针对不同的线搜索参数分别讨论了共轭梯度法簇的全局收敛性。

Parametric evaluation function can condense the complicated multi-objective and multi-constrait problem into a single-objective unconstrained problem, then solved with conjugate gradient method.The parametric evaluation function method decreases the optimization difficulty,and makes the solution away from the boundary of feasible region during the optimization process,which makes all the objectives optimized simultaneously.

参数评价函数把复杂的多目标多约束优化问题转化为单目标无约束问题,然后用共轭梯度法进行求解,不但降低了求解难度,而且使得在优化过程中其解远离可行域的边界,保证了对多目标中所有目标同时进行优化。

For this theory, it is better than the traditional fixed damping conjugate gradient method.

从理论上讲,它要优于传统的固定阻尼共轭梯度法

This is a realization of optimal calculation procedure, the algorithm used to conjugate gradient method.

这是一个实现最优化计算的程序,采用的算法为共轭梯度法

The conjugate gradient method is one of the most efficient methods for solving unconstrained optimization problems.

摘要共轭梯度法是求解无约束优化问题的一类有效方法。

The numerical experiments shows that the algorithm is much more efficient than the general genetic algorithm and conjugate gradient method.

算例结果表明,该算法对于病态方程组的求解效果明显优于一般的遗传算法和共轭梯度法

Conjugate gradient method is a method for solving nonlinear optimization problems, especially large-scale problems.

共轭梯度法是求解无约束优化问题的一种重要的方法,尤其适用于大规模优化问题的求解。

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